Machine learning is a subfield of artificial intelligence that focuses on developing algorithms and models capable of automatically learning and making predictions or decisions from data without being explicitly programmed. It involves training models on labeled datasets to recognize patterns and make accurate predictions or classifications in new, unseen data.
This study introduces a deep learning-based Motor Assessment Model (MAM) designed to automate General Movement Assessment (GMA) in infants, predicting the risk of cerebral palsy (CP). The MAM, utilizing 3D pose estimation and Transformer architecture, demonstrated high accuracy, sensitivity, and specificity in identifying fidgety movements, essential for CP risk assessment. With interpretability, the model aids GMA beginners and holds promise for streamlined, accessible, and early CP screening, potentially transforming video-based diagnostics for infant motor abnormalities.
This paper emphasizes the crucial role of machine learning (ML) in detecting and combating fake news amid the proliferation of misinformation on social media. The study reviews various ML techniques, including deep learning, natural language processing (NLP), ensemble learning, transfer learning, and graph-based approaches, highlighting their strengths and limitations in fake news detection. The researchers advocate for a multifaceted strategy, combining different techniques and optimizing computational strategies to address the complex challenges of identifying misinformation in the digital age.
This study introduces innovative unsupervised machine-learning techniques to analyze and interpret high-resolution global storm-resolving models (GSRMs). By leveraging variational autoencoders and vector quantization, the researchers systematically break down massive datasets, uncover spatiotemporal patterns, identify inconsistencies among GSRMs, and even project the impact of climate change on storm dynamics.
This article covers breakthroughs and innovations in natural language processing, computer vision, and data security. From addressing logical reasoning challenges with the discourse graph attention network to advancements in text classification using BERT models, lightweight mask detection in computer vision, sports analytics employing network graph theory, and data security through image steganography, the authors showcase the broad impact of AI across various domains.
Researchers advocate for employing artificial neural networks (ANNs) as "artificial physics engines" to compute complex inverse dynamics in human arm and hand movements. The study showcases ANNs' potential in enhancing assistive technologies, such as prosthetics and exoskeletons, offering a detailed, customizable, and reactive approach for more natural movement in individuals with impaired motor function.
This study introduces a Digital Twin (DT)-centered Fire Safety Management (FSM) framework for smart buildings. Harnessing technologies like AI, IoT, AR, and BIM, the framework enhances decision-making, real-time information access, and FSM efficiency. Evaluation by Facility Management professionals affirms its effectiveness, with a majority expressing confidence in its clarity, data security, and utility for fire evacuation planning and Fire Safety Equipment (FSE) maintenance.
This research addresses the challenge of customer churn in the banking sector using Genetic Algorithm eXtreme Gradient Boosting (GA-XGBoost). The study emphasizes the significance of techniques like SMOTEENN in handling data imbalances and introduces the SHAP interpretation framework for model interpretability. The optimized GA-XGBoost model proves effective in predicting customer churn, offering valuable insights for proactive customer retention strategies in the dynamic banking landscape.
Researchers introduced a hybrid Ridge Generative Adversarial Network (RidgeGAN) model to predict road network density in small and medium-sized Indian cities under the Integrated Development of Small and Medium Towns (IDSMT) project. Integrating City Generative Adversarial Network (CityGAN) and Kernel Ridge Regression (KRR), the model successfully generated realistic urban patterns, aiding urban planners in optimizing layouts for efficient transportation infrastructure development.
Researchers from Nanjing University of Science and Technology present a novel scheme, Spatial Variation-Dependent Verification (SVV), utilizing convolutional neural networks and textural features for handwriting identification and verification. The scheme outperforms existing methods, achieving 95.587% accuracy, providing a robust solution for secure handwriting recognition and authentication in diverse applications, including security, forensics, banking, education, and healthcare.
Researchers propose an Improved Deep Reinforcement Learning (IDRL) approach to optimize routing paths and reduce control overhead in Vehicular Ad-Hoc Networks (VANETs). The IDRL routing technique leverages deep reinforcement learning to dynamically determine routes based on transmission capacity and vehicular data, resulting in reduced latency, improved data reliability, and increased packet delivery ratio (PDR) compared to existing routing techniques.
Researchers employ deep neural networks and machine learning to predict facial landmarks and pain scores in cats using the Feline Grimace Scale. The study demonstrates advanced CNN models accurately predicting facial landmarks and an XGBoost model achieving high accuracy in discerning painful and non-painful cats. This breakthrough paves the way for an automated smartphone application, addressing the challenge of non-verbal pain assessment in felines and marking a significant advancement in veterinary care.
Researchers employed cutting-edge cloud computing and machine learning on Google Earth Engine to create a vast global land cover training dataset. This meticulous resource spans nearly four decades, encompassing diverse biogeographic regions and addressing challenges in existing global datasets. The GLanCE dataset's validation process, utilizing sophisticated machine learning techniques, ensures data accuracy while highlighting the complexities and challenges in distinguishing specific land cover categories even at a 30-meter spatial resolution.
This article delves into bolstering Internet of Things (IoT) security, specifically countering botnet attacks that jeopardize IoT ecosystems. Employing tree-based algorithms, including Decision Trees, Random Forest, and boosting techniques, the researchers conduct a thorough empirical analysis, highlighting Random Forest's standout multi-class classification accuracy and superior computational efficiency.
This paper introduces an innovative method for real-time measurement of dynamic accommodation and vergence in the human eye, utilizing Purkinje reflections and machine learning. The technique offers high accuracy, addressing limitations in current measurement methods and holds significant potential for applications in vision diagnostics, 3D displays, and customized virtual reality headsets. The study's comprehensive analysis showcases the efficacy of the proposed approach, emphasizing its simplicity, accuracy, and adaptability for diverse ophthalmic and technological settings.
This article presents a novel workflow for generating high-resolution lithology logs from conventional well logs, addressing challenges in multiclass imbalanced data classification. The enhanced weighted average ensemble approach, incorporating error-correcting output code (ECOC) and cost-sensitive learning (CSL) techniques, outperforms traditional machine learning algorithms.
This scientific report explores the potential of mega-castings to replace steel sheets in automotive structures, offering cost efficiency and design flexibility. Researchers propose a novel two-phase optimization pipeline combining topology optimization, response-surface-based techniques, and machine learning to balance crash demands, castability, and structural goals. The approach outperforms traditional workflows, generating weight-optimized designs within shorter timeframes.
Researchers proposed an IoT and ML-based approach to analyze ornamental goldfish behavior in response to environmental changes, particularly real-time water temperature and dissolved oxygen concentration. Utilizing IoT sensors and machine learning classifiers like Decision Tree, Naïve Bayes, Linear Discriminant Analysis, and K-Nearest Neighbor, the study demonstrated the effectiveness of the Decision Tree classifier in accurately classifying behavioral changes.
Researchers detail a groundbreaking approach for creating realistic train-and-test datasets to evaluate machine learning models in software bug assignments. The novel method, based on time dependencies, addresses limitations in existing techniques, ensuring more reliable assessments in real-world scenarios. The proposed method offers potential applications in telecommunication, software quality prediction, and maintenance, contributing to the development of bug-free software applications.
This paper delves into the transformative impact of machine learning (ML) in scientific research while highlighting critical challenges, particularly in COVID-19 diagnostics using AI-driven algorithms. The study underscores concerns about misleading claims, flawed methodologies, and the need for standardized guidelines to ensure credibility and reproducibility. It addresses issues such as data leakage, inadequate reporting, and overstatement of findings, emphasizing the importance of proper training and standardized methodologies in the rapidly evolving field of health-related ML.
This article delves into the unequal impacts of automation on urban and non-urban labor markets. Analyzing Italian data, it reveals that automation universally displaces workers, challenging assumptions about urban immunity. The study emphasizes the need for ongoing research to understand the evolving landscape of automation, its impact on workforce composition, and the potential for exacerbating inequalities between privileged urban occupations and excluded low-skill workers.
Terms
While we only use edited and approved content for Azthena
answers, it may on occasions provide incorrect responses.
Please confirm any data provided with the related suppliers or
authors. We do not provide medical advice, if you search for
medical information you must always consult a medical
professional before acting on any information provided.
Your questions, but not your email details will be shared with
OpenAI and retained for 30 days in accordance with their
privacy principles.
Please do not ask questions that use sensitive or confidential
information.
Read the full Terms & Conditions.